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Depth-enhanced high-throughput microscopy by compact PSF engineering.
Opatovski, Nadav; Nehme, Elias; Zoref, Noam; Barzilai, Ilana; Orange Kedem, Reut; Ferdman, Boris; Keselman, Paul; Alalouf, Onit; Shechtman, Yoav.
Afiliação
  • Opatovski N; Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel.
  • Nehme E; Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
  • Zoref N; Department of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
  • Barzilai I; Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
  • Orange Kedem R; Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
  • Ferdman B; Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel.
  • Keselman P; Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel.
  • Alalouf O; Sartorius Stedim North America Inc., Bohemia, NY, USA.
  • Shechtman Y; Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Nat Commun ; 15(1): 4861, 2024 Jun 07.
Article em En | MEDLINE | ID: mdl-38849376
ABSTRACT
High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article